Image contrast is usually achieved by rearranging the distribution of gray levels in an image scene. Increasing the contrast of an image is accomplished by making the dark portions of the image darker and the light portions of the image lighter. The effect of redistributing the gray levels is often visually beneficial because redistribution of gray levels uses more of an available dynamic range of a black to white intensity spectrum. Often the redistribution of energy is performed with the aid of a non-linear function.
Many approaches have been devised to improve the contrast of an image. Histogram equalization is a method that has been used to alter the contrast of an image by attempting to redistribute the gray level energy in the image such that the output distribution is uniform. However, histogram equalization suffers from a serious drawback because the effects of full equalization are usually too severe to be visually pleasing. This method has some preferred uses such as in medical imaging, where colour balance is not always as important as accentuating gray level detail. In video and film processing, however, histogram equalization is not used because maintaining a proper proportion of gray level through the image spectrum is important.
More recently, adaptive histogram equalization has received much attention. The basic idea behind adaptive histogram equalization is to generate a histogram of pixel intensities in an image and normalize them to produce a cumulative density function (CDF). Then, an altered version of the CDF is used to map the intensity, or gray level, data in an image to change the contrast. Even more sophisticated methods use local histogram information and local image statistics on a region of the image in order to achieve the desired context-dependent level of contrast enhancement.
However, all of the state of the art solutions to the problem of contrast enhancement still have results that have room for improvement. Therefore, it desirable that to develop a system and a method for providing an image that is more visually pleasing than the original and can be readily implemented on a microprocessor.